model,
inits,
fim - inicio,
tnames,
pnames,
wl,
nw,
verbose=1,
burnin=1000,
constraints=[])
F.set_priors(tdists=[st.beta, st.uniform, st.uniform],
tpars=tpars,
tlims=tlims,
pdists=[st.beta] * nph,
ppars=[(1, 1)] * nph,
plims=[(0, 1)] * nph)
F.run(dt,
'DREAM',
likvar=1e-9,
pool=False,
ew=0,
adjinits=True,
dbname=modname,
monitor=['I', 'S'])
# ~ print F.AIC, F.BIC, F.DIC
# print F.optimize(data=dt,p0=[s0,s1,s2], optimizer='scipy',tol=1e-55, verbose=1, plot=1)
# F.plot_results(['S', 'I'], dbname=modname, savefigs=1)
P.clf()
P.clf()
ser = st.nanmean(series, axis=0)
# print series.shape, ser.shape
return ser
d = runModel([beta, alpha, sigma])
# ~ import pylab as P
# ~ P.plot(d)
# ~ P.show()
dt = {'S': d[:, 0], 'E': d[:, 1], 'I': d[:, 2], 'A': d[:, 3], 'R': d[:, 4]}
F = FitModel(900,
runModel,
inits,
tf, ['beta', 'alpha', 'sigma'], ['S', 'E', 'I', 'A', 'R'],
wl=140,
nw=1,
verbose=0,
burnin=100)
F.set_priors(tdists=[st.uniform] * 3,
tpars=[(0.00001, .0006), (.1, .5), (0.0006, 1)],
tlims=[(0, .001), (.001, 1), (0, 1)],
pdists=[st.uniform] * 5,
ppars=[(0, 500)] * 5,
plims=[(0, 500)] * 5)
F.run(dt, 'MCMC', likvar=1e1, pool=0, monitor=[])
# ~ print F.optimize(data=dt,p0=[0.1,.5,.1], optimizer='oo',tol=1e-55, verbose=1, plot=1)
# ==Uncomment the line below to see plots of the results
F.plot_results()
return [
-beta * I * S, #dS/dt
beta * I * S - tau * I, #dI/dt
tau * I
] #dR/dt
y = odeint(sir, inits, np.arange(0, tf, 1)) #np.arange(t0,tf,step))
return y
F = FitModel(300,
model,
y0,
tf, ['beta'], ['S', 'I', 'R'],
wl=36,
nw=1,
verbose=1,
burnin=100)
F.set_priors(tdists=[st.norm],
tpars=[(1.1, .2)],
tlims=[(0.5, 1.5)],
pdists=[st.uniform] * 3,
ppars=[(0, .1), (0, .1), (.8, .2)],
plims=[(0, 1)] * 3)
d = model([1.0]) #simulate some data
noise = st.norm(0, 0.01).rvs(36)
dt = {'I': d[:, 1] + noise} # add noise
F.run(dt, 'MCMC', likvar=1e-5, pool=True, monitor=[])
#==Uncomment the line below to see plots of the results
F.plot_results()
beta = 1 #Transmission coefficient
tau = .2 #infectious period. FIXED
tf = 36
y0 = [.999, 0.001, 0.0]
def model(theta):
beta = theta[0]
def sir(y, t):
'''ODE model'''
S, I, R = y
return [-beta * I * S, #dS/dt
beta * I * S - tau * I, #dI/dt
tau * I] #dR/dt
y = odeint(sir, inits, np.arange(0, tf, 1)) #np.arange(t0,tf,step))
return y
F = FitModel(300, model, y0, tf, ['beta'], ['S', 'I', 'R'],
wl=36, nw=1, verbose=1, burnin=100)
F.set_priors(tdists=[st.norm], tpars=[(1.1, .2)], tlims=[(0.5, 1.5)],
pdists=[st.uniform] * 3, ppars=[(0, .1), (0, .1), (.8, .2)], plims=[(0, 1)] * 3)
d = model([1.0]) #simulate some data
noise = st.norm(0, 0.01).rvs(36)
dt = {'I': d[:, 1] + noise} # add noise
F.run(dt, 'MCMC', likvar=1e-5, pool=True, monitor=[])
#==Uncomment the line below to see plots of the results
F.plot_results()
tmat = np.array([[-1, 0, 0, 0, 0],
[ 1,-1, 0,-1, 0],
[ 0, 1,-1, 0, 0],
[ 0, 0, 0, 1,-1],
[ 0, 0, 1, 0, 1]
])
M=Model(vnames=vnames,rates = r,inits=inits,tmat=tmat,propensity=propf)
#t0 = time.time()
M.run(tmax=tf,reps=1,viz=0,serial=True)
t,series,steps,events = M.getStats()
ser = st.nanmean(series,axis=0)
#print series.shape, ser.shape
return ser
d = runModel([beta,alpha,sigma])
#~ import pylab as P
#~ P.plot(d)
#~ P.show()
dt = {'S':d[:,0],'E':d[:,1],'I':d[:,2],'A':d[:,3],'R':d[:,4]}
F = FitModel(900, runModel,inits,tf,['beta','alpha','sigma'],['S','E','I','A','R'],
wl=140,nw=1,verbose=0,burnin=100)
F.set_priors(tdists=[st.uniform]*3,tpars=[(0.00001,.0006),(.1,.5),(0.0006,1)],tlims=[(0,.001),(.001,1),(0,1)],
pdists=[st.uniform]*5,ppars=[(0,500)]*5,plims=[(0,500)]*5)
F.run(dt,'MCMC',likvar=1e1,pool=0,monitor=[])
#~ print F.optimize(data=dt,p0=[0.1,.5,.1], optimizer='oo',tol=1e-55, verbose=1, plot=1)
#==Uncomment the line below to see plots of the results
F.plot_results()
nw,
verbose=1,
burnin=1100,
constraints=[])
F.set_priors(tdists=[st.beta, st.norm],
tpars=tpars,
tlims=tlims,
pdists=[st.beta] * nph,
ppars=[(1, 1)] * nph,
plims=[(0, 1)] * nph)
F.run(dt,
'DREAM',
likvar=1e-8,
likfun='Normal',
pool=False,
ew=0,
adjinits=True,
dbname=modname,
monitor=['I', 'S'],
initheta=[0.0621, 1e-6])
# ~ print(F.AIC, F.BIC, F.DIC)
# print (F.optimize(data=dt2, p0=[.0621, 1e-6, 1.0], optimizer='scipy',tol=1e-55, verbose=1, plot=1))
F.plot_results(['S', 'I'], dbname=modname, savefigs=1)
P.figure()
P.figure()
P.plot(dt['time'], y[:, 1], label='I')
plot_data(modname)
P.clf()
P.clf()
return [
-beta * I * S, # dS/dt
beta * I * S - tau * I, # dI/dt
tau * I
] # dR/dt
y = odeint(sir, inits, np.arange(0, tf, 1))
return y
F = FitModel(2000,
model,
y0,
tf, ['beta', 'tau'], ['S', 'I', 'R'],
wl=36,
nw=1,
verbose=1,
burnin=100)
F.set_priors(tdists=[st.norm, st.norm],
tpars=[(1.1, .2), (.2, .1)],
tlims=[(0.5, 1.5), (0, 1)],
pdists=[st.uniform] * 3,
ppars=[(0, .1), (0, .1), (.8, .2)],
plims=[(0, 1)] * 3)
d = model([1.0, .2]) # simulate some data
noise = st.norm(0, 0.01).rvs(36)
dt = {'I': d[:, 1] + noise} # add noise
F.run(dt, 'DREAM', likvar=1e-2, pool=True, monitor=['I'], likfun='Normal')
# ==Uncomment the line below to see plots of the results
F.plot_results()
